74 A recent study has demonstrated the effectiveness of using the Extreme Gradient Boosting (XGBoost) machine learning algorithm in accurately classifying breast cancer cells. With an accuracy rate of 94.74%, this algorithm presents a promising tool in the early detection and diagnosis of one of the most prevalent and deadly cancers affecting women worldwide. The findings were published in the World Journal of Advanced Research and Reviews. Breast cancer is a leading cause of death among women, second only to lung cancer. The disease manifests through various symptoms, such as persistent pain, changes in breast size and skin texture, and the presence of lumps. Early and accurate diagnosis is crucial for improving survival rates, which remain dismally low, with only about 14% of diagnosed individuals surviving beyond five years. To improve diagnosis, researchers have increasingly turned to artificial intelligence (AI) and machine learning techniques. These technologies have the ability to quickly and effectively process large datasets, potentially detecting cancerous changes that human eyes might miss. Among these techniques, XGBoost has emerged as a particularly effective method due to its ability to handle complex data and provide robust predictions. The study utilised the Wisconsin Breast Cancer Diagnostic dataset, which includes 569 instances of breast cells, with each instance labelled as either benign (non-cancerous) or malignant (cancerous). The dataset is made up of 30 features that describe the features of cellular nuclei. These features were found in digital images of fine needle aspirates of breast masses. Researchers divided the dataset into training and testing subsets, with 80% of the data used to train the model and the remaining 20% used for testing. The XGBoost algorithm was chosen because it can handle complex relationships between many features, does not suffer from multicollinearity (which happens when model variables that are supposed to be independent are strongly linked), and can process data quickly thanks to its parallel processing features. During the training process, the algorithm was fine-tuned using specific hyperparameters, such as a learning rate of 0.3 and a maximum depth of 4. These settings helped the model achieve its high accuracy rate. The model was also tested for precision, recall, and F1 score—key metrics in evaluating the performance of classification models. The results were impressive, with the model achieving a precision score of 90.91%, a recall score of 95.24%, and an F1 score of 93.02%. One of the standout features of the XGBoost model is its ability to identify which features of the breast cells are most important in distinguishing between benign and malignant cases. According to the study, the most significant features were “concave points_worst” and “area_worst”, both of which received the highest F-scores, indicating their critical role in the model’s predictive accuracy. Other important features included “texture_worst” and “area_se”, which also contributed significantly to the model’s performance. The study also compared the XGBoost model with other machine learning algorithms previously used for breast cancer classification. The comparison revealed that XGBoost outperformed several other models, including Support Vector Machine (SVM), Random Forest, and Logistic Regression, which had accuracy rates ranging from 61.85% to 94.4%. The study highlighted that XGBoost not only matched but exceeded the performance of these models, making it a more reliable tool for breast cancer detection. The findings of this study are significant for the future of breast cancer diagnosis. The high accuracy and efficiency of the XGBoost model suggest that it could be integrated into clinical settings to assist pathologists in making faster and more accurate diagnoses. This could be particularly valuable in regions where access to specialised medical professionals is limited, as the model could be used to support less experienced clinicians in making critical decisions. Moreover, the ability of the XGBoost model to highlight the most important predictive features can provide valuable insights into the biological characteristics that differentiate malignant from benign cells. This knowledge could further inform research into breast cancer, potentially leading to the development of new diagnostic tools or therapies. While the study demonstrates the effectiveness of the XGBoost algorithm, the researchers note that there is room for improvement. They suggest that future studies could explore the use of larger datasets and incorporate additional functionalities, such as detecting the specific phase of breast cancer. Such advancements could enhance the model’s accuracy and broaden its clinical applications.